Registered S3 methods overwritten by 'htmltools':
  method               from         
  print.html           tools:rstudio
  print.shiny.tag      tools:rstudio
  print.shiny.tag.list tools:rstudio
Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio
library(groundhog)
Loaded 'groundhog' (version:2.0.1) using R-4.2.1
Tips and troubleshooting: https://groundhogR.com
groundhog says:

          OUTDATED GROUNDHOG
            You are using version  '2.0.1
            The current version is '2.1.0'

            You can read about the changes here: https://groundhogr.com/changelog

Update by running: 
install.packages('groundhog')
pkgs <-  c("tidyverse","here", "lmerTest", "sjPlot","broom.mixed", "kableExtra", "ggeffects", "gt", "brms", "bayestestR","ggdist", "pheatmap", "heatmaply","pheatmap","gplots","RColorBrewer")
groundhog.day <- '2022-07-25'
groundhog.library(pkgs, groundhog.day)
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     
Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
Registered S3 methods overwritten by 'readr':
  method                    from 
  as.data.frame.spec_tbl_df vroom
  as_tibble.spec_tbl_df     vroom
  format.col_spec           vroom
  print.col_spec            vroom
  print.collector           vroom
  print.date_names          vroom
  print.locale              vroom
  str.col_spec              vroom
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──✔ ggplot2 3.3.6     ✔ purrr   0.3.4
✔ tibble  3.1.8     ✔ dplyr   1.0.9
✔ tidyr   1.2.0     ✔ stringr 1.4.0
✔ readr   2.1.2     ✔ forcats 0.5.1── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()Succesfully attached 'tidyverse_1.3.2'
here() starts at /Users/jacobelder/Documents/GitHub/EpMemNet
Succesfully attached 'here_1.0.1'
Loading required package: lme4
Loading required package: Matrix

Attaching package: ‘Matrix’

The following objects are masked from ‘package:tidyr’:

    expand, pack, unpack


Attaching package: ‘lmerTest’

The following object is masked from ‘package:lme4’:

    lmer

The following object is masked from ‘package:stats’:

    step

Succesfully attached 'lmerTest_3.1-3'
Learn more about sjPlot with 'browseVignettes("sjPlot")'.
Succesfully attached 'sjPlot_2.8.10'
Succesfully attached 'broom.mixed_0.2.9.4'

Attaching package: ‘kableExtra’

The following object is masked from ‘package:dplyr’:

    group_rows

Succesfully attached 'kableExtra_1.3.4'
Succesfully attached 'ggeffects_1.1.2'
Succesfully attached 'gt_0.6.0'
Loading required package: Rcpp
Loading 'brms' package (version 2.17.0). Useful instructions
can be found by typing help('brms'). A more detailed introduction
to the package is available through vignette('brms_overview').

Attaching package: ‘brms’

The following object is masked from ‘package:lme4’:

    ngrps

The following object is masked from ‘package:stats’:

    ar

Succesfully attached 'brms_2.17.0'
Succesfully attached 'bayestestR_0.12.1'

Attaching package: ‘ggdist’

The following object is masked from ‘package:bayestestR’:

    hdi

The following objects are masked from ‘package:brms’:

    dstudent_t, pstudent_t, qstudent_t, rstudent_t

Succesfully attached 'ggdist_3.2.0'
Succesfully attached 'pheatmap_1.0.12'
Registered S3 method overwritten by 'seriation':
  method         from 
  reorder.hclust gclus
Loading required package: plotly

Attaching package: ‘plotly’

The following object is masked from ‘package:ggplot2’:

    last_plot

The following object is masked from ‘package:stats’:

    filter

The following object is masked from ‘package:graphics’:

    layout

Loading required package: viridis
Loading required package: viridisLite

======================
Welcome to heatmaply version 1.3.0

Type citation('heatmaply') for how to cite the package.
Type ?heatmaply for the main documentation.

The github page is: https://github.com/talgalili/heatmaply/
Please submit your suggestions and bug-reports at: https://github.com/talgalili/heatmaply/issues
You may ask questions at stackoverflow, use the r and heatmaply tags: 
     https://stackoverflow.com/questions/tagged/heatmaply
======================

Succesfully attached 'heatmaply_1.3.0'
The package 'pheatmap_1.0.12' is already attached.

Attaching package: ‘gplots’

The following object is masked from ‘package:stats’:

    lowess

Succesfully attached 'gplots_3.1.3'
Succesfully attached 'RColorBrewer_1.1-3'
here::i_am("Analysis/idmPrelimAnal.Rmd")
here() starts at /Users/jacobelder/Documents/GitHub/EpMemNet
devtools::source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/corToOne.R")
ℹ SHA-1 hash of file is "07e3c11d2838efe15b1a6baf5ba2694da3f28cb1"
devtools::source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/plotCommAxes.R")
ℹ SHA-1 hash of file is "374a4de7fec345d21628a52c0ed0e4f2c389df8e"
devtools::source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/named.effects.ref.R")
ℹ SHA-1 hash of file is "0a5b928a75d310573e96ab72631b77a8a8b9acb3"
fullLong <- arrow::read_parquet(here("Data", "longEpMNet.parquet"))
fullShort <- arrow::read_parquet(here("Data","shortEpMNet.parquet"))
fullLong$subID <- as.numeric(fullLong$subID)
fullData <- fullLong %>% full_join(fullShort, by = c("subID"))

Wordcloud

#Create a vector containing only the text
text <- as.vector(fullData$memory)
# Create a corpus  
docs <- Corpus(VectorSource(text))
docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
Warning: transformation drops documentsWarning: transformation drops documentsWarning: transformation drops documents
docs <- tm_map(docs, content_transformer(tolower))
Warning: transformation drops documents
docs <- tm_map(docs, removeWords, stopwords("english"))
Warning: transformation drops documents
docs <- tm_map(docs, removeWords, c("the","and"))
Warning: transformation drops documents
dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df <- data.frame(word = names(words),freq=words)

wordcloud(words = df$word, freq = df$freq, min.freq = 1,           max.words=200, random.order=FALSE, rot.per=0.35,            colors=brewer.pal(8, "Dark2"))

H1: People will evaluate more positively, less negatively (i.e., more favorably) on memories with more downstram dependents.

summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Val_2 ~ outdegree + indegree + (outdegree + indegree | subID)
   Data: fullData

REML criterion at convergence: 15341.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.8750 -0.7838 -0.1704  0.7634  2.5234 

Random effects:
 Groups   Name        Variance  Std.Dev. Corr       
 subID    (Intercept)  300.7515 17.3422             
          outdegree      0.7721  0.8787  -0.13      
          indegree       6.2828  2.5065  -0.65  0.83
 Residual             1036.5181 32.1950             
Number of obs: 1548, groups:  subID, 207

Fixed effects:
            Estimate Std. Error       df t value Pr(>|t|)    
(Intercept)  42.3059     1.9074 125.5175  22.180   <2e-16 ***
outdegree     0.3745     0.4632 126.7401   0.808    0.420    
indegree     -0.8963     0.5666  34.2498  -1.582    0.123    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
          (Intr) outdgr
outdegree -0.390       
indegree  -0.513  0.017
optimizer (nloptwrap) convergence code: 0 (OK)
unable to evaluate scaled gradient
Model failed to converge: degenerate  Hessian with 1 negative eigenvalues

H2: People will be more certain in memories with more downstream dependents.

m<-lmer(Cert ~  outdegree * indegree + ( outdegree + indegree | subID), data=fullData)
Warning: Model failed to converge with max|grad| = 0.00237505 (tol = 0.002, component 1)

H3: Memories with more dependents will be more clearly defined and accessible.

summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Clear ~ outdegree * indegree + (outdegree + indegree | subID)
   Data: fullData

REML criterion at convergence: 6299.3

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-5.1269 -0.4734  0.1503  0.6291  3.2023 

Random effects:
 Groups   Name        Variance Std.Dev. Corr       
 subID    (Intercept) 0.549521 0.74130             
          outdegree   0.013024 0.11412  -0.44      
          indegree    0.001925 0.04387  -0.02 -0.45
 Residual             1.022438 1.01116             
Number of obs: 2065, groups:  subID, 211

Fixed effects:
                    Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)          5.78616    0.06838 231.33961  84.616   <2e-16 ***
outdegree            0.03795    0.02018 138.44596   1.880   0.0622 .  
indegree             0.03698    0.01917  93.96452   1.929   0.0567 .  
outdegree:indegree  -0.00338    0.00403  32.54608  -0.839   0.4077    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) outdgr indegr
outdegree   -0.491              
indegree    -0.381  0.154       
outdgr:ndgr  0.300 -0.441 -0.666
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 0.00215486 (tol = 0.002, component 1)

H5: Memories with more dependents will be more fundamental to how people see themselves, and if they were changed, would change the person.

summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ outdegree * indegree + (outdegree + indegree | subID)
   Data: fullData

REML criterion at convergence: 7715.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2162 -0.5220  0.1034  0.6270  2.5481 

Random effects:
 Groups   Name        Variance Std.Dev. Corr       
 subID    (Intercept) 1.06273  1.0309              
          outdegree   0.01338  0.1157   -0.75      
          indegree    0.00126  0.0355   -0.01  0.31
 Residual             2.07307  1.4398              
Number of obs: 2067, groups:  subID, 211

Fixed effects:
                     Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)          4.789805   0.095453 240.070368  50.180  < 2e-16 ***
outdegree            0.190555   0.024236  82.278464   7.862 1.28e-11 ***
indegree             0.056842   0.025617  80.036007   2.219   0.0293 *  
outdegree:indegree  -0.008254   0.005521  25.194016  -1.495   0.1473    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) outdgr indegr
outdegree   -0.583              
indegree    -0.400  0.332       
outdgr:ndgr  0.302 -0.498 -0.705

H6: Memories with more dependents will be more important to the person.

To Self

summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: IM ~ outdegree * indegree + (outdegree + indegree | subID)
   Data: fullData

REML criterion at convergence: 6672.9

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.5370 -0.4050  0.1854  0.6154  2.4371 

Random effects:
 Groups   Name        Variance Std.Dev. Corr       
 subID    (Intercept) 0.516692 0.71881             
          outdegree   0.011485 0.10717  -0.57      
          indegree    0.000857 0.02927  -0.39  0.39
 Residual             1.260789 1.12285             
Number of obs: 2068, groups:  subID, 211

Fixed effects:
                     Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)          5.747140   0.070259 256.346056  81.799  < 2e-16 ***
outdegree            0.085016   0.020503 109.905889   4.147 6.67e-05 ***
indegree             0.052310   0.019705  70.486561   2.655  0.00981 ** 
outdegree:indegree  -0.006379   0.004403  29.066621  -1.449  0.15816    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) outdgr indegr
outdegree   -0.560              
indegree    -0.448  0.298       
outdgr:ndgr  0.317 -0.455 -0.703
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 0.00201471 (tol = 0.002, component 1)

To Others

H9: People’s self-report of retrospected emotions during an experience will be associated with how positively or negatively they perceive the experience.

summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Often ~ outdegree * indegree + (outdegree + indegree | subID)
   Data: fullData

REML criterion at convergence: 7625.4

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.5030 -0.6726 -0.0755  0.6849  2.7581 

Random effects:
 Groups   Name        Variance Std.Dev. Corr       
 subID    (Intercept) 1.196272 1.09374             
          outdegree   0.029577 0.17198  -0.34      
          indegree    0.001086 0.03296  -0.86 -0.20
 Residual             2.011366 1.41823             
Number of obs: 2043, groups:  subID, 211

Fixed effects:
                     Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)         4.388e+00  9.872e-02  2.487e+02  44.454   <2e-16 ***
outdegree           7.477e-02  2.908e-02  1.060e+02   2.571   0.0115 *  
indegree            4.829e-02  2.426e-02  9.785e+02   1.990   0.0468 *  
outdegree:indegree -5.575e-04  5.133e-03  1.384e+02  -0.109   0.9137    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) outdgr indegr
outdegree   -0.467              
indegree    -0.432  0.218       
outdgr:ndgr  0.307 -0.397 -0.766
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')

H11: People think more often about memories with more memories causing them.

The more memories that depend on a given memory, the more people believe “This memory changed me”

summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ outdegree * indegree + (outdegree + indegree | subID)
   Data: fullData

REML criterion at convergence: 7357.6

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.6619 -0.5006  0.1219  0.6297  2.7562 

Random effects:
 Groups   Name        Variance  Std.Dev. Corr       
 subID    (Intercept) 0.9236523 0.96107             
          outdegree   0.0081336 0.09019  -0.79      
          indegree    0.0005095 0.02257  -0.20  0.76
 Residual             1.7462980 1.32148             
Number of obs: 2067, groups:  subID, 211

Fixed effects:
                     Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)          5.102015   0.088091 250.970752  57.918  < 2e-16 ***
outdegree            0.144625   0.021061 172.110844   6.867 1.14e-10 ***
indegree             0.032704   0.022870 321.632075   1.430    0.154    
outdegree:indegree  -0.004158   0.004877  42.964756  -0.852    0.399    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) outdgr indegr
outdegree   -0.581              
indegree    -0.408  0.381       
outdgr:ndgr  0.304 -0.516 -0.732
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')

The more memories that depend on a given memory, the more certain that people feel this experience is representative of who they are.

summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Rep ~ outdegree * indegree + (outdegree + indegree | subID)
   Data: fullData

REML criterion at convergence: 7571.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.3850 -0.5395  0.0710  0.6483  3.0274 

Random effects:
 Groups   Name        Variance Std.Dev. Corr       
 subID    (Intercept) 1.105868 1.05160             
          outdegree   0.011932 0.10923  -0.49      
          indegree    0.006731 0.08204  -0.30 -0.02
 Residual             1.892707 1.37576             
Number of obs: 2067, groups:  subID, 211

Fixed effects:
                     Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)          4.738014   0.095535 258.205125  49.595  < 2e-16 ***
outdegree            0.124056   0.025023 129.805289   4.958 2.19e-06 ***
indegree             0.082239   0.027056 126.365172   3.040  0.00288 ** 
outdegree:indegree  -0.008269   0.005843 143.902843  -1.415  0.15915    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) outdgr indegr
outdegree   -0.479              
indegree    -0.434  0.246       
outdgr:ndgr  0.284 -0.525 -0.609
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 0.00496817 (tol = 0.002, component 1)

Exploratory Analyses

Sentiment of memory will be associated with dependencies

summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: vad_neg.x ~ outdegree + indegree + (outdegree + indegree | subID)
   Data: fullData

REML criterion at convergence: -2395.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.2736 -0.4135 -0.3078 -0.1937  6.5171 

Random effects:
 Groups   Name        Variance  Std.Dev. Corr       
 subID    (Intercept) 1.464e-03 0.038259            
          outdegree   2.728e-05 0.005223 -0.02      
          indegree    4.000e-06 0.002000 -1.00  0.00
 Residual             1.924e-02 0.138703            
Number of obs: 2281, groups:  subID, 217

Fixed effects:
              Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)  5.678e-02  5.343e-03  1.304e+02  10.628   <2e-16 ***
outdegree   -7.797e-04  1.728e-03  8.273e+01  -0.451    0.653    
indegree    -8.582e-06  1.406e-03  2.234e+02  -0.006    0.995    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
          (Intr) outdgr
outdegree -0.405       
indegree  -0.404 -0.198
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: vad_pos.x ~ indegree * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: -1317.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.3643 -0.5609 -0.4600  0.2573  4.9936 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.00110  0.03317 
 Residual             0.03142  0.17727 
Number of obs: 2281, groups:  subID, 217

Fixed effects:
                     Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)         8.725e-02  7.031e-03  6.158e+02  12.409  < 2e-16 ***
indegree            9.378e-03  2.688e-03  2.126e+03   3.488 0.000496 ***
outdegree           4.411e-04  2.347e-03  1.959e+03   0.188 0.850934    
indegree:outdegree -1.158e-03  5.544e-04  2.219e+03  -2.089 0.036804 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) indegr outdgr
indegree    -0.581              
outdegree   -0.586  0.299       
indegr:tdgr  0.479 -0.749 -0.566
m<-lmer(IM ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
Warning: Model failed to converge with max|grad| = 0.0153971 (tol = 0.002, component 1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: IM ~ outdegree + indegree + (outdegree + indegree | subID)
   Data: fullData

REML criterion at convergence: 6665.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.5445 -0.4073  0.1879  0.6203  2.4769 

Random effects:
 Groups   Name        Variance  Std.Dev. Corr       
 subID    (Intercept) 0.5169032 0.71896             
          outdegree   0.0114980 0.10723  -0.56      
          indegree    0.0003298 0.01816  -0.68  0.57
 Residual             1.2638297 1.12420             
Number of obs: 2068, groups:  subID, 211

Fixed effects:
             Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)   5.77913    0.06630 173.76150  87.160  < 2e-16 ***
outdegree     0.07156    0.01827  82.83632   3.917 0.000183 ***
indegree      0.03221    0.01290   2.05403   2.496 0.126674    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
          (Intr) outdgr
outdegree -0.491       
indegree  -0.328 -0.046
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 0.0153971 (tol = 0.002, component 1)
m<-lmer(IM ~  strength + ( strength | subID), data=fullData)
Warning: Model failed to converge with max|grad| = 92.0221 (tol = 0.002, component 1)Warning: Model is nearly unidentifiable: very large eigenvalue
 - Rescale variables?
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: IM ~ strength + (strength | subID)
   Data: fullData

REML criterion at convergence: 6687.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.5559 -0.4118  0.1778  0.5887  2.5612 

Random effects:
 Groups   Name        Variance  Std.Dev. Corr 
 subID    (Intercept) 1.199e+00 1.095211      
          strength    1.916e-06 0.001384 -0.83
 Residual             1.201e+00 1.096010      
Number of obs: 2068, groups:  subID, 211

Fixed effects:
             Estimate Std. Error        df t value Pr(>|t|)    
(Intercept) 5.794e+00  8.705e-02 7.182e+01  66.563  < 2e-16 ***
strength    8.144e-04  1.814e-04 3.410e+01   4.489 7.77e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
         (Intr)
strength -0.670
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 92.0221 (tol = 0.002, component 1)
Model is nearly unidentifiable: very large eigenvalue
 - Rescale variables?
m<-lmer(IO ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(IO ~  strength + ( strength | subID), data=fullData)
summary(m)
m<-glmer(outdegree ~  Val_1*Val_2 + ( Val_1+Val_2 | subID), data=fullData, family="poisson")
summary(m)
ggpredict(m, terms = c("Val_1","Val_2")) %>% plot()
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Val_1 ~ outdegree * indegree + (outdegree + indegree | subID)
   Data: fullData

REML criterion at convergence: 18149.2

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.0913 -0.4947  0.2810  0.6610  2.0754 

Random effects:
 Groups   Name        Variance Std.Dev. Corr       
 subID    (Intercept) 251.939  15.873              
          outdegree     3.624   1.904   -0.44      
          indegree      2.134   1.461   -0.61  0.02
 Residual             810.698  28.473              
Number of obs: 1879, groups:  subID, 210

Fixed effects:
                    Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)         70.24459    1.70457 196.80392  41.210   <2e-16 ***
outdegree           -0.07589    0.51228 101.96304  -0.148   0.8825    
indegree             1.22299    0.53295  83.93875   2.295   0.0242 *  
outdegree:indegree  -0.05458    0.11869  37.70184  -0.460   0.6483    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) outdgr indegr
outdegree   -0.513              
indegree    -0.534  0.243       
outdgr:ndgr  0.354 -0.545 -0.652
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 0.00493827 (tol = 0.002, component 1)
m<-lmer(Val_2 ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(Val_2 ~  strength + ( strength | subID), data=fullData)
summary(m)
m<-lmer(Clear ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(Clear ~  strength + ( strength | subID), data=fullData)
summary(m)
m<-lmer(Breadth ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(Breadth ~  strength + ( strength | subID), data=fullData)
summary(m)
m<-lmer(Dist ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(Dist ~  strength + ( strength | subID), data=fullData)
summary(m)
m<-glmer(outdegree ~  scale(SO_1) * scale(SO_2) + ( 1 | subID), data=fullData,family="poisson")
summary(m)
ggpredict(m, terms = c("SO_1","SO_2")) %>% plot()
m<-lmer( Fund ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ SE * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7683.3

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2098 -0.5290  0.1137  0.6371  2.4864 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.7864   0.8868  
 Residual             2.1379   1.4622  
Number of obs: 2055, groups:  subID, 208

Fixed effects:
              Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)  4.275e+00  3.501e-01 2.761e+02  12.212   <2e-16 ***
SE           2.581e-01  1.528e-01 2.804e+02   1.689   0.0923 .  
outdegree    1.361e-01  6.979e-02 2.031e+03   1.950   0.0513 .  
SE:outdegree 2.583e-02  3.257e-02 2.030e+03   0.793   0.4279    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) SE     outdgr
SE          -0.974              
outdegree   -0.333  0.335       
SE:outdegre  0.313 -0.335 -0.967
m<-lmer( Fund ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ SAM * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7549.2

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2175 -0.5178  0.1063  0.6419  2.5699 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.7361   0.858   
 Residual             2.1389   1.462   
Number of obs: 2021, groups:  subID, 205

Fixed effects:
                Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)      3.65517    0.34689  295.35502  10.537  < 2e-16 ***
SAM              0.38011    0.10665  285.84081   3.564 0.000428 ***
outdegree        0.24059    0.09253 2016.77967   2.600 0.009385 ** 
SAM:outdegree   -0.01742    0.02775 2015.59102  -0.628 0.530306    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) SAM    outdgr
SAM         -0.974              
outdegree   -0.360  0.347       
SAM:outdegr  0.356 -0.358 -0.981
m<-lmer( Fund ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ CESD * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7684.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.1745 -0.5336  0.1117  0.6490  2.6458 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.7868   0.887   
 Residual             2.1391   1.463   
Number of obs: 2055, groups:  subID, 208

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)       4.22605    0.34348  277.88771  12.304  < 2e-16 ***
CESD              0.28387    0.15012  272.11324   1.891 0.059696 .  
outdegree         0.26814    0.08006 2030.09868   3.349 0.000825 ***
CESD:outdegree   -0.03549    0.03441 2025.53893  -1.031 0.302512    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) CESD   outdgr
CESD        -0.973              
outdegree   -0.346  0.334       
CESD:outdgr  0.339 -0.345 -0.975
m<-lmer( Fund ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ SOS * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7684.4

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2164 -0.5324  0.1076  0.6382  2.5541 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.7801   0.8832  
 Residual             2.1389   1.4625  
Number of obs: 2055, groups:  subID, 208

Fixed effects:
               Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)   4.151e+00  3.397e-01 2.693e+02  12.219  < 2e-16 ***
SOS           2.418e-01  1.133e-01 2.680e+02   2.134  0.03372 *  
outdegree     1.879e-01  6.649e-02 2.012e+03   2.826  0.00476 ** 
SOS:outdegree 5.252e-04  2.321e-02 2.013e+03   0.023  0.98195    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) SOS    outdgr
SOS         -0.973              
outdegree   -0.329  0.326       
SOS:outdegr  0.310 -0.329 -0.964
m<-lmer( Fund ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ DS * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7684.2

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.1906 -0.5193  0.0969  0.6308  2.4225 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.7976   0.8931  
 Residual             2.1357   1.4614  
Number of obs: 2055, groups:  subID, 208

Fixed effects:
               Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)     5.12833    0.49225  278.82633  10.418   <2e-16 ***
DS             -0.06920    0.12263  274.79489  -0.564   0.5730    
outdegree      -0.04272    0.10639 2039.19106  -0.402   0.6881    
DS:outdegree    0.05965    0.02711 2035.46763   2.200   0.0279 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) DS     outdgr
DS          -0.987              
outdegree   -0.335  0.334       
DS:outdegre  0.326 -0.335 -0.986
m<-lmer( Fund ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ MAIA * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7687.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.1646 -0.5155  0.1022  0.6426  2.3918 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.7984   0.8935  
 Residual             2.1391   1.4626  
Number of obs: 2055, groups:  subID, 208

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)       4.48539    0.47849  250.19012   9.374  < 2e-16 ***
MAIA              0.09801    0.12482  248.35032   0.785 0.433076    
outdegree         0.29410    0.08819 2035.27152   3.335 0.000869 ***
MAIA:outdegree   -0.02706    0.02199 2036.91686  -1.231 0.218639    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) MAIA   outdgr
MAIA        -0.986              
outdegree   -0.328  0.314       
MAIA:outdgr  0.329 -0.327 -0.980
m<-lmer( Fund ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ DT_P * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7674.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.1650 -0.5145  0.1130  0.6400  2.7859 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.8107   0.9004  
 Residual             2.1224   1.4569  
Number of obs: 2055, groups:  subID, 208

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)       4.55068    0.31153  276.69546  14.607  < 2e-16 ***
DT_P              0.13186    0.12454  283.89718   1.059 0.290599    
outdegree         0.45316    0.07210 2035.70085   6.285    4e-10 ***
DT_P:outdegree   -0.11384    0.03001 2043.27314  -3.793 0.000153 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) DT_P   outdgr
DT_P        -0.967              
outdegree   -0.341  0.339       
DT_P:outdgr  0.325 -0.344 -0.969
m<-lmer( Fund ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ DT_M * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7686.3

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.1699 -0.5168  0.1039  0.6356  2.6418 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.7911   0.8894  
 Residual             2.1387   1.4624  
Number of obs: 2055, groups:  subID, 208

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)       4.22306    0.36808  238.74422  11.473  < 2e-16 ***
DT_M              0.20404    0.11487  243.51099   1.776 0.076945 .  
outdegree         0.25252    0.06569 2011.41871   3.844 0.000125 ***
DT_M:outdegree   -0.02176    0.02146 2027.70946  -1.014 0.310826    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) DT_M   outdgr
DT_M        -0.977              
outdegree   -0.306  0.306       
DT_M:outdgr  0.293 -0.314 -0.963
m<-lmer( Fund ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ NFC * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7689.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.1673 -0.5207  0.1023  0.6387  2.5883 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.8035   0.8964  
 Residual             2.1396   1.4627  
Number of obs: 2055, groups:  subID, 208

Fixed effects:
                Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)      4.90463    0.41350  267.01092  11.861   <2e-16 ***
NFC             -0.01102    0.10471  264.95407  -0.105    0.916    
outdegree        0.13822    0.08849 2018.72965   1.562    0.118    
NFC:outdegree    0.01219    0.02123 2017.83732   0.574    0.566    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) NFC    outdgr
NFC         -0.981              
outdegree   -0.322  0.305       
NFC:outdegr  0.323 -0.319 -0.980
m<-lmer( Fund ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ SCC * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7682

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.1759 -0.5091  0.1079  0.6446  2.5120 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.7679   0.8763  
 Residual             2.1385   1.4624  
Number of obs: 2055, groups:  subID, 208

Fixed effects:
                Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)      5.58979    0.31225  267.98502  17.902  < 2e-16 ***
SCC             -0.25145    0.10347  271.53806  -2.430  0.01574 *  
outdegree        0.22293    0.06974 2024.18454   3.197  0.00141 ** 
SCC:outdegree   -0.01136    0.02241 2019.77420  -0.507  0.61230    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) SCC    outdgr
SCC         -0.968              
outdegree   -0.336  0.318       
SCC:outdegr  0.328 -0.333 -0.967
fullData$PminN <- (fullData$Val_1-fullData$Val_2)
m<-lmer( PminN ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: PminN ~ SE * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 15421.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.5881 -0.5455  0.2077  0.7854  1.9727 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept)  382.2   19.55   
 Residual             3372.3   58.07   
Number of obs: 1399, groups:  subID, 202

Fixed effects:
             Estimate Std. Error       df t value Pr(>|t|)    
(Intercept)    60.469     12.079  337.154   5.006 8.97e-07 ***
SE            -13.544      5.269  350.723  -2.570   0.0106 *  
outdegree       4.393      3.285 1336.362   1.337   0.1813    
SE:outdegree   -2.352      1.526 1337.904  -1.541   0.1235    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) SE     outdgr
SE          -0.975              
outdegree   -0.519  0.520       
SE:outdegre  0.487 -0.517 -0.970
m<-lmer( PminN ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: PminN ~ SAM * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 15138.6

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.4973 -0.5590  0.2111  0.7756  1.9338 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept)  427.1   20.67   
 Residual             3416.4   58.45   
Number of obs: 1371, groups:  subID, 199

Fixed effects:
              Estimate Std. Error       df t value Pr(>|t|)   
(Intercept)     39.075     12.710  343.570   3.074  0.00228 **
SAM             -3.170      3.849  324.713  -0.824  0.41074   
outdegree        8.295      4.203 1217.855   1.973  0.04867 * 
SAM:outdegree   -2.557      1.239 1175.036  -2.064  0.03926 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) SAM    outdgr
SAM         -0.976              
outdegree   -0.524  0.500       
SAM:outdegr  0.515 -0.514 -0.982
m<-lmer( PminN ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: PminN ~ CESD * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 15416.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.5919 -0.5456  0.1928  0.7810  1.9627 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept)  377.3   19.43   
 Residual             3362.7   57.99   
Number of obs: 1399, groups:  subID, 202

Fixed effects:
               Estimate Std. Error       df t value Pr(>|t|)    
(Intercept)      47.371     11.856  331.376   3.996 7.95e-05 ***
CESD             -8.022      5.160  329.247  -1.554  0.12103    
outdegree        10.696      3.709 1362.306   2.884  0.00399 ** 
CESD:outdegree   -4.841      1.611 1356.346  -3.006  0.00270 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) CESD   outdgr
CESD        -0.974              
outdegree   -0.524  0.512       
CESD:outdgr  0.513 -0.527 -0.977
m<-lmer( PminN ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: PminN ~ SOS * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 15423.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.5548 -0.5384  0.1973  0.7751  1.9573 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept)  393.3   19.83   
 Residual             3369.6   58.05   
Number of obs: 1399, groups:  subID, 202

Fixed effects:
              Estimate Std. Error       df t value Pr(>|t|)    
(Intercept)     50.892     11.857  339.136   4.292 2.31e-05 ***
SOS             -7.240      3.957  344.618  -1.830   0.0681 .  
outdegree        6.449      3.265 1389.701   1.975   0.0484 *  
SOS:outdegree   -2.448      1.129 1383.971  -2.168   0.0303 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) SOS    outdgr
SOS         -0.974              
outdegree   -0.525  0.517       
SOS:outdegr  0.498 -0.519 -0.970
m<-lmer( PminN ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: PminN ~ DS * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 15436.4

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.5300 -0.5476  0.2096  0.7680  1.9172 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept)  423.9   20.59   
 Residual             3388.6   58.21   
Number of obs: 1399, groups:  subID, 202

Fixed effects:
              Estimate Std. Error        df t value Pr(>|t|)   
(Intercept)    53.2900    17.3987  341.9508   3.063  0.00237 **
DS             -5.9663     4.3111  336.9454  -1.384  0.16729   
outdegree       1.0452     5.0343 1263.5368   0.208  0.83556   
DS:outdegree   -0.3525     1.2831 1290.2909  -0.275  0.78357   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) DS     outdgr
DS          -0.988              
outdegree   -0.527  0.526       
DS:outdegre  0.511 -0.524 -0.987
m<-lmer( PminN ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: PminN ~ MAIA * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 15424.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.6524 -0.5416  0.2132  0.7783  1.9695 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept)  367.2   19.16   
 Residual             3384.2   58.17   
Number of obs: 1399, groups:  subID, 202

Fixed effects:
                Estimate Std. Error        df t value Pr(>|t|)   
(Intercept)     -15.5434    15.4593  281.7684  -1.005  0.31555   
MAIA             11.9574     4.0030  271.2232   2.987  0.00307 **
outdegree        -3.2176     3.9571 1334.6026  -0.813  0.41629   
MAIA:outdegree    0.7158     0.9825 1319.9343   0.729  0.46642   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) MAIA   outdgr
MAIA        -0.985              
outdegree   -0.530  0.509       
MAIA:outdgr  0.530 -0.528 -0.980
m<-lmer( PminN ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: PminN ~ DT_P * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 15435.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.5128 -0.5631  0.2061  0.7639  1.8896 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept)  409.3   20.23   
 Residual             3392.6   58.25   
Number of obs: 1399, groups:  subID, 202

Fixed effects:
                Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)      49.9051    10.9751  372.2452   4.547 7.37e-06 ***
DT_P             -8.4208     4.3826  390.9773  -1.921   0.0554 .  
outdegree        -1.2834     3.3680 1304.6733  -0.381   0.7032    
DT_P:outdegree    0.4197     1.3930 1254.0608   0.301   0.7632    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) DT_P   outdgr
DT_P        -0.969              
outdegree   -0.541  0.535       
DT_P:outdgr  0.517 -0.543 -0.972
m<-lmer( PminN ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: PminN ~ DT_M * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 15438.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.5339 -0.5573  0.2062  0.7749  1.9375 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept)  438.4   20.94   
 Residual             3386.4   58.19   
Number of obs: 1399, groups:  subID, 202

Fixed effects:
                Estimate Std. Error        df t value Pr(>|t|)
(Intercept)      14.1544    12.7997  304.7340   1.106    0.270
DT_M              4.8324     3.9790  308.7030   1.214    0.225
outdegree         1.1927     3.1749 1387.9516   0.376    0.707
DT_M:outdegree   -0.4546     1.0161 1363.0202  -0.447    0.655

Correlation of Fixed Effects:
            (Intr) DT_M   outdgr
DT_M        -0.977              
outdegree   -0.509  0.502       
DT_M:outdgr  0.487 -0.508 -0.968
m<-lmer( PminN ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: PminN ~ NFC * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 15440

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.5255 -0.5526  0.2089  0.7777  1.9309 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept)  447.1   21.14   
 Residual             3384.9   58.18   
Number of obs: 1399, groups:  subID, 202

Fixed effects:
               Estimate Std. Error        df t value Pr(>|t|)  
(Intercept)     30.7547    14.3671  331.3007   2.141    0.033 *
NFC             -0.3257     3.6284  323.6829  -0.090    0.929  
outdegree       -2.8860     3.9391 1393.7161  -0.733    0.464  
NFC:outdegree    0.6554     0.9528 1394.1076   0.688    0.492  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) NFC    outdgr
NFC         -0.981              
outdegree   -0.498  0.476       
NFC:outdegr  0.498 -0.497 -0.979
m<-lmer( PminN ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: PminN ~ SCC * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 15430.4

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.5363 -0.5486  0.2086  0.7702  1.9420 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept)  414.7   20.36   
 Residual             3375.8   58.10   
Number of obs: 1399, groups:  subID, 202

Fixed effects:
              Estimate Std. Error       df t value Pr(>|t|)  
(Intercept)     17.156     10.955  333.903   1.566   0.1183  
SCC              4.230      3.649  335.967   1.159   0.2471  
outdegree       -6.460      3.272 1372.178  -1.974   0.0485 *
SCC:outdegree    2.076      1.070 1385.385   1.940   0.0526 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) SCC    outdgr
SCC         -0.969              
outdegree   -0.507  0.489       
SCC:outdegr  0.498 -0.512 -0.970
m<-lmer( Fund ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ SE * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7683.3

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2098 -0.5290  0.1137  0.6371  2.4864 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.7864   0.8868  
 Residual             2.1379   1.4622  
Number of obs: 2055, groups:  subID, 208

Fixed effects:
              Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)  4.275e+00  3.501e-01 2.761e+02  12.212   <2e-16 ***
SE           2.581e-01  1.528e-01 2.804e+02   1.689   0.0923 .  
outdegree    1.361e-01  6.979e-02 2.031e+03   1.950   0.0513 .  
SE:outdegree 2.583e-02  3.257e-02 2.030e+03   0.793   0.4279    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) SE     outdgr
SE          -0.974              
outdegree   -0.333  0.335       
SE:outdegre  0.313 -0.335 -0.967
m<-lmer( Fund ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ SAM * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7549.2

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2175 -0.5178  0.1063  0.6419  2.5699 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.7361   0.858   
 Residual             2.1389   1.462   
Number of obs: 2021, groups:  subID, 205

Fixed effects:
                Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)      3.65517    0.34689  295.35502  10.537  < 2e-16 ***
SAM              0.38011    0.10665  285.84081   3.564 0.000428 ***
outdegree        0.24059    0.09253 2016.77967   2.600 0.009385 ** 
SAM:outdegree   -0.01742    0.02775 2015.59102  -0.628 0.530306    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) SAM    outdgr
SAM         -0.974              
outdegree   -0.360  0.347       
SAM:outdegr  0.356 -0.358 -0.981
m<-lmer( Fund ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ CESD * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7684.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.1745 -0.5336  0.1117  0.6490  2.6458 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.7868   0.887   
 Residual             2.1391   1.463   
Number of obs: 2055, groups:  subID, 208

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)       4.22605    0.34348  277.88771  12.304  < 2e-16 ***
CESD              0.28387    0.15012  272.11324   1.891 0.059696 .  
outdegree         0.26814    0.08006 2030.09868   3.349 0.000825 ***
CESD:outdegree   -0.03549    0.03441 2025.53893  -1.031 0.302512    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) CESD   outdgr
CESD        -0.973              
outdegree   -0.346  0.334       
CESD:outdgr  0.339 -0.345 -0.975
m<-lmer( Fund ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ SOS * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7684.4

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2164 -0.5324  0.1076  0.6382  2.5541 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.7801   0.8832  
 Residual             2.1389   1.4625  
Number of obs: 2055, groups:  subID, 208

Fixed effects:
               Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)   4.151e+00  3.397e-01 2.693e+02  12.219  < 2e-16 ***
SOS           2.418e-01  1.133e-01 2.680e+02   2.134  0.03372 *  
outdegree     1.879e-01  6.649e-02 2.012e+03   2.826  0.00476 ** 
SOS:outdegree 5.252e-04  2.321e-02 2.013e+03   0.023  0.98195    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) SOS    outdgr
SOS         -0.973              
outdegree   -0.329  0.326       
SOS:outdegr  0.310 -0.329 -0.964
m<-lmer( Fund ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ DS * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7684.2

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.1906 -0.5193  0.0969  0.6308  2.4225 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.7976   0.8931  
 Residual             2.1357   1.4614  
Number of obs: 2055, groups:  subID, 208

Fixed effects:
               Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)     5.12833    0.49225  278.82633  10.418   <2e-16 ***
DS             -0.06920    0.12263  274.79489  -0.564   0.5730    
outdegree      -0.04272    0.10639 2039.19106  -0.402   0.6881    
DS:outdegree    0.05965    0.02711 2035.46763   2.200   0.0279 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) DS     outdgr
DS          -0.987              
outdegree   -0.335  0.334       
DS:outdegre  0.326 -0.335 -0.986
m<-lmer( Fund ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ MAIA * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7687.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.1646 -0.5155  0.1022  0.6426  2.3918 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.7984   0.8935  
 Residual             2.1391   1.4626  
Number of obs: 2055, groups:  subID, 208

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)       4.48539    0.47849  250.19012   9.374  < 2e-16 ***
MAIA              0.09801    0.12482  248.35032   0.785 0.433076    
outdegree         0.29410    0.08819 2035.27152   3.335 0.000869 ***
MAIA:outdegree   -0.02706    0.02199 2036.91686  -1.231 0.218639    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) MAIA   outdgr
MAIA        -0.986              
outdegree   -0.328  0.314       
MAIA:outdgr  0.329 -0.327 -0.980
m<-lmer( Fund ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ DT_P * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7674.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.1650 -0.5145  0.1130  0.6400  2.7859 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.8107   0.9004  
 Residual             2.1224   1.4569  
Number of obs: 2055, groups:  subID, 208

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)       4.55068    0.31153  276.69546  14.607  < 2e-16 ***
DT_P              0.13186    0.12454  283.89718   1.059 0.290599    
outdegree         0.45316    0.07210 2035.70085   6.285    4e-10 ***
DT_P:outdegree   -0.11384    0.03001 2043.27314  -3.793 0.000153 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) DT_P   outdgr
DT_P        -0.967              
outdegree   -0.341  0.339       
DT_P:outdgr  0.325 -0.344 -0.969
m<-lmer( Fund ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ DT_M * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7686.3

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.1699 -0.5168  0.1039  0.6356  2.6418 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.7911   0.8894  
 Residual             2.1387   1.4624  
Number of obs: 2055, groups:  subID, 208

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)       4.22306    0.36808  238.74422  11.473  < 2e-16 ***
DT_M              0.20404    0.11487  243.51099   1.776 0.076945 .  
outdegree         0.25252    0.06569 2011.41871   3.844 0.000125 ***
DT_M:outdegree   -0.02176    0.02146 2027.70946  -1.014 0.310826    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) DT_M   outdgr
DT_M        -0.977              
outdegree   -0.306  0.306       
DT_M:outdgr  0.293 -0.314 -0.963
m<-lmer( Fund ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ NFC * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7689.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.1673 -0.5207  0.1023  0.6387  2.5883 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.8035   0.8964  
 Residual             2.1396   1.4627  
Number of obs: 2055, groups:  subID, 208

Fixed effects:
                Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)      4.90463    0.41350  267.01092  11.861   <2e-16 ***
NFC             -0.01102    0.10471  264.95407  -0.105    0.916    
outdegree        0.13822    0.08849 2018.72965   1.562    0.118    
NFC:outdegree    0.01219    0.02123 2017.83732   0.574    0.566    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) NFC    outdgr
NFC         -0.981              
outdegree   -0.322  0.305       
NFC:outdegr  0.323 -0.319 -0.980
m<-lmer( Fund ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Fund ~ SCC * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7682

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.1759 -0.5091  0.1079  0.6446  2.5120 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.7679   0.8763  
 Residual             2.1385   1.4624  
Number of obs: 2055, groups:  subID, 208

Fixed effects:
                Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)      5.58979    0.31225  267.98502  17.902  < 2e-16 ***
SCC             -0.25145    0.10347  271.53806  -2.430  0.01574 *  
outdegree        0.22293    0.06974 2024.18454   3.197  0.00141 ** 
SCC:outdegree   -0.01136    0.02241 2019.77420  -0.507  0.61230    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) SCC    outdgr
SCC         -0.968              
outdegree   -0.336  0.318       
SCC:outdegr  0.328 -0.333 -0.967
m<-lmer( Chan ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ SE * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7313.3

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.6328 -0.5180  0.1212  0.6408  2.4006 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.6904   0.8309  
 Residual             1.7782   1.3335  
Number of obs: 2055, groups:  subID, 208

Fixed effects:
              Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)  4.584e+00  3.247e-01 2.738e+02  14.117   <2e-16 ***
SE           2.382e-01  1.417e-01 2.778e+02   1.681   0.0939 .  
outdegree    9.624e-02  6.373e-02 2.027e+03   1.510   0.1312    
SE:outdegree 2.984e-02  2.974e-02 2.026e+03   1.003   0.3158    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) SE     outdgr
SE          -0.974              
outdegree   -0.327  0.329       
SE:outdegre  0.307 -0.329 -0.967
m<-lmer( Chan ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ SAM * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7183.9

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.6473 -0.5208  0.1206  0.6371  2.4250 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.6394   0.7996  
 Residual             1.7793   1.3339  
Number of obs: 2021, groups:  subID, 205

Fixed effects:
               Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)   4.156e+00  3.205e-01 2.914e+02  12.970  < 2e-16 ***
SAM           3.060e-01  9.855e-02 2.822e+02   3.105  0.00209 ** 
outdegree     9.430e-02  8.452e-02 2.017e+03   1.116  0.26467    
SAM:outdegree 1.808e-02  2.535e-02 2.017e+03   0.713  0.47577    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) SAM    outdgr
SAM         -0.974              
outdegree   -0.355  0.342       
SAM:outdegr  0.351 -0.353 -0.981
m<-lmer( Chan ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ CESD * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7314.2

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.6559 -0.5191  0.1332  0.6463  2.4014 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.6856   0.828   
 Residual             1.7801   1.334   
Number of obs: 2055, groups:  subID, 208

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)       4.46452    0.31787  276.49130  14.045  < 2e-16 ***
CESD              0.29616    0.13895  270.86164   2.131  0.03396 *  
outdegree         0.19516    0.07311 2026.81987   2.669  0.00766 ** 
CESD:outdegree   -0.01721    0.03143 2022.05907  -0.548  0.58408    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) CESD   outdgr
CESD        -0.973              
outdegree   -0.341  0.328       
CESD:outdgr  0.334 -0.339 -0.975
m<-lmer( Chan ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ SOS * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7314.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.6383 -0.5085  0.1240  0.6448  2.4067 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.6817   0.8257  
 Residual             1.7795   1.3340  
Number of obs: 2055, groups:  subID, 208

Fixed effects:
               Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)   4.480e+00  3.147e-01 2.671e+02  14.236   <2e-16 ***
SOS           2.192e-01  1.050e-01 2.659e+02   2.088   0.0377 *  
outdegree     1.252e-01  6.070e-02 2.008e+03   2.062   0.0393 *  
SOS:outdegree 1.182e-02  2.119e-02 2.009e+03   0.558   0.5771    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) SOS    outdgr
SOS         -0.973              
outdegree   -0.323  0.320       
SOS:outdegr  0.305 -0.323 -0.964
m<-lmer( Chan ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ DS * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7313.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.6174 -0.5013  0.1230  0.6416  2.3948 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.7004   0.8369  
 Residual             1.7757   1.3325  
Number of obs: 2055, groups:  subID, 208

Fixed effects:
               Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)     5.19054    0.45657  276.35989  11.369   <2e-16 ***
DS             -0.01804    0.11376  272.49764  -0.159   0.8741    
outdegree      -0.07279    0.09715 2035.36866  -0.749   0.4538    
DS:outdegree    0.05934    0.02475 2031.38317   2.397   0.0166 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) DS     outdgr
DS          -0.987              
outdegree   -0.328  0.328       
DS:outdegre  0.320 -0.329 -0.986
m<-lmer( Chan ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ MAIA * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7318.2

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.5986 -0.5204  0.1230  0.6409  2.3870 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.7004   0.8369  
 Residual             1.7798   1.3341  
Number of obs: 2055, groups:  subID, 208

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)       4.81662    0.44413  248.77477  10.845   <2e-16 ***
MAIA              0.08051    0.11586  246.98514   0.695   0.4878    
outdegree         0.25949    0.08055 2031.60048   3.221   0.0013 ** 
MAIA:outdegree   -0.02624    0.02009 2033.26793  -1.306   0.1917    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) MAIA   outdgr
MAIA        -0.986              
outdegree   -0.321  0.308       
MAIA:outdgr  0.323 -0.321 -0.980
m<-lmer( Chan ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ DT_P * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7309.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.6221 -0.5225  0.1448  0.6421  2.4822 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.7054   0.8399  
 Residual             1.7709   1.3307  
Number of obs: 2055, groups:  subID, 208

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)       5.10155    0.28835  274.20611  17.692  < 2e-16 ***
DT_P              0.01278    0.11526  281.15601   0.111  0.91182    
outdegree         0.35222    0.06593 2032.84483   5.342 1.02e-07 ***
DT_P:outdegree   -0.08412    0.02745 2040.98547  -3.065  0.00221 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) DT_P   outdgr
DT_P        -0.967              
outdegree   -0.336  0.334       
DT_P:outdgr  0.320 -0.339 -0.969
m<-lmer( Chan ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ DT_M * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7318.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.6061 -0.5128  0.1281  0.6473  2.4047 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.6905   0.831   
 Residual             1.7814   1.335   
Number of obs: 2055, groups:  subID, 208

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)     4.638e+00  3.413e-01  2.365e+02  13.590  < 2e-16 ***
DT_M            1.552e-01  1.065e-01  2.411e+02   1.457  0.14642    
outdegree       1.821e-01  6.001e-02  2.007e+03   3.034  0.00245 ** 
DT_M:outdegree -8.553e-03  1.961e-02  2.024e+03  -0.436  0.66279    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) DT_M   outdgr
DT_M        -0.977              
outdegree   -0.300  0.301       
DT_M:outdgr  0.288 -0.309 -0.963
m<-lmer( Chan ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ NFC * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7320.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.6132 -0.5153  0.1282  0.6462  2.4070 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.7018   0.8378  
 Residual             1.7808   1.3345  
Number of obs: 2055, groups:  subID, 208

Fixed effects:
               Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)   4.944e+00  3.831e-01 2.655e+02  12.905   <2e-16 ***
NFC           4.697e-02  9.702e-02 2.635e+02   0.484   0.6287    
outdegree     1.394e-01  8.081e-02 2.015e+03   1.725   0.0846 .  
NFC:outdegree 4.115e-03  1.939e-02 2.014e+03   0.212   0.8319    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) NFC    outdgr
NFC         -0.981              
outdegree   -0.317  0.300       
NFC:outdegr  0.318 -0.314 -0.980
m<-lmer( Chan ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ SCC * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 7310.6

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.6373 -0.5116  0.1253  0.6378  2.4107 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.6713   0.8193  
 Residual             1.7781   1.3334  
Number of obs: 2055, groups:  subID, 208

Fixed effects:
                Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)      5.83642    0.28929  267.27206  20.175  < 2e-16 ***
SCC             -0.24528    0.09585  270.73889  -2.559 0.011044 *  
outdegree        0.21139    0.06366 2020.25543   3.321 0.000914 ***
SCC:outdegree   -0.01797    0.02045 2015.82415  -0.879 0.379653    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) SCC    outdgr
SCC         -0.968              
outdegree   -0.330  0.312       
SCC:outdegr  0.323 -0.327 -0.967
m<-lmer( Cert ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Cert ~ SE * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 6411.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.5927 -0.5513  0.1285  0.5886  2.7481 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.5678   0.7535  
 Residual             1.1658   1.0797  
Number of obs: 2032, groups:  subID, 208

Fixed effects:
               Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)     5.75297    0.28324  259.29915  20.311   <2e-16 ***
SE             -0.10839    0.12354  262.42119  -0.877    0.381    
outdegree       0.07994    0.05186 1983.57725   1.541    0.123    
SE:outdegree   -0.01546    0.02421 1982.27728  -0.639    0.523    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) SE     outdgr
SE          -0.974              
outdegree   -0.301  0.303       
SE:outdegre  0.282 -0.303 -0.967
m<-lmer( Cert ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Cert ~ SAM * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 6283.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.0666 -0.5461  0.1432  0.5925  2.7687 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.5636   0.7507  
 Residual             1.1526   1.0736  
Number of obs: 1998, groups:  subID, 205

Fixed effects:
                Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)      5.04752    0.28422  271.64450  17.760   <2e-16 ***
SAM              0.14529    0.08753  264.36833   1.660   0.0981 .  
outdegree        0.15488    0.06885 1983.29226   2.250   0.0246 *  
SAM:outdegree   -0.03216    0.02066 1986.07005  -1.557   0.1197    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) SAM    outdgr
SAM         -0.974              
outdegree   -0.320  0.308       
SAM:outdegr  0.316 -0.319 -0.982
m<-lmer( Cert ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Cert ~ CESD * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 6412

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.6236 -0.5465  0.1304  0.5875  2.7673 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.5747   0.7581  
 Residual             1.1652   1.0794  
Number of obs: 2032, groups:  subID, 208

Fixed effects:
                Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)    5.403e+00  2.789e-01 2.606e+02  19.371   <2e-16 ***
CESD           4.733e-02  1.221e-01 2.561e+02   0.388    0.698    
outdegree      1.377e-02  5.950e-02 1.982e+03   0.231    0.817    
CESD:outdegree 1.527e-02  2.558e-02 1.977e+03   0.597    0.551    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) CESD   outdgr
CESD        -0.973              
outdegree   -0.313  0.301       
CESD:outdgr  0.306 -0.311 -0.975
m<-lmer( Cert ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Cert ~ SOS * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 6414

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.6101 -0.5436  0.1300  0.5814  2.7495 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.5749   0.7582  
 Residual             1.1656   1.0796  
Number of obs: 2032, groups:  subID, 208

Fixed effects:
                Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)    5.513e+00  2.771e-01  2.533e+02  19.896   <2e-16 ***
SOS           -1.982e-03  9.244e-02  2.521e+02  -0.021    0.983    
outdegree      4.139e-02  4.936e-02  1.962e+03   0.838    0.402    
SOS:outdegree  2.580e-03  1.723e-02  1.962e+03   0.150    0.881    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) SOS    outdgr
SOS         -0.973              
outdegree   -0.294  0.291       
SOS:outdegr  0.277 -0.294 -0.963
m<-lmer( Cert ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Cert ~ DS * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 6408.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.6060 -0.5401  0.1304  0.5970  2.7560 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.5549   0.7449  
 Residual             1.1660   1.0798  
Number of obs: 2032, groups:  subID, 208

Fixed effects:
               Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)   6.323e+00  3.938e-01  2.657e+02  16.055   <2e-16 ***
DS           -2.057e-01  9.816e-02  2.626e+02  -2.096   0.0371 *  
outdegree     5.013e-02  7.922e-02  1.995e+03   0.633   0.5269    
DS:outdegree -5.535e-04  2.019e-02  1.991e+03  -0.027   0.9781    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) DS     outdgr
DS          -0.987              
outdegree   -0.308  0.307       
DS:outdegre  0.300 -0.309 -0.986
m<-lmer( Cert ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Cert ~ MAIA * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 6404.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.5952 -0.5469  0.1288  0.6050  2.7598 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.5408   0.7354  
 Residual             1.1656   1.0796  
Number of obs: 2032, groups:  subID, 208

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)     4.368e+00  3.805e-01  2.443e+02  11.478  < 2e-16 ***
MAIA            3.016e-01  9.927e-02  2.424e+02   3.038  0.00264 ** 
outdegree       7.443e-02  6.573e-02  1.995e+03   1.132  0.25764    
MAIA:outdegree -6.885e-03  1.638e-02  1.997e+03  -0.420  0.67428    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) MAIA   outdgr
MAIA        -0.986              
outdegree   -0.304  0.291       
MAIA:outdgr  0.305 -0.303 -0.980
m<-lmer( Cert ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Cert ~ DT_P * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 6408.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.6144 -0.5376  0.1364  0.5934  2.7674 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.5591   0.7478  
 Residual             1.1654   1.0795  
Number of obs: 2032, groups:  subID, 208

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)       6.02338    0.24867  262.42292  24.223   <2e-16 ***
DT_P             -0.21320    0.09934  268.39906  -2.146   0.0328 *  
outdegree         0.02214    0.05376 1994.70771   0.412   0.6805    
DT_P:outdegree    0.01111    0.02240 2004.76783   0.496   0.6199    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) DT_P   outdgr
DT_P        -0.967              
outdegree   -0.315  0.313       
DT_P:outdgr  0.300 -0.318 -0.969
m<-lmer( Cert ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Cert ~ DT_M * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 6413.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.6149 -0.5481  0.1351  0.5941  2.7617 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.5728   0.7568  
 Residual             1.1653   1.0795  
Number of obs: 2032, groups:  subID, 208

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)       5.40232    0.30003  231.45381  18.006   <2e-16 ***
DT_M              0.03481    0.09354  235.17116   0.372   0.7101    
outdegree         0.09691    0.04878 1961.86630   1.987   0.0471 *  
DT_M:outdegree   -0.01644    0.01595 1979.03059  -1.031   0.3029    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) DT_M   outdgr
DT_M        -0.977              
outdegree   -0.273  0.274       
DT_M:outdgr  0.262 -0.281 -0.963
m<-lmer( Cert ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Cert ~ NFC * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 6409.2

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.6107 -0.5452  0.1406  0.5826  2.7588 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.5605   0.7486  
 Residual             1.1648   1.0793  
Number of obs: 2032, groups:  subID, 208

Fixed effects:
                Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)    4.775e+00  3.314e-01  2.587e+02  14.409   <2e-16 ***
NFC            1.891e-01  8.396e-02  2.568e+02   2.253   0.0251 *  
outdegree      7.113e-02  6.580e-02  1.975e+03   1.081   0.2799    
NFC:outdegree -5.794e-03  1.578e-02  1.974e+03  -0.367   0.7135    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) NFC    outdgr
NFC         -0.981              
outdegree   -0.296  0.279       
NFC:outdegr  0.296 -0.292 -0.980
m<-lmer( Cert ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Cert ~ SCC * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 6412.9

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.6124 -0.5531  0.1417  0.5784  2.7739 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.5717   0.7561  
 Residual             1.1653   1.0795  
Number of obs: 2032, groups:  subID, 208

Fixed effects:
                Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)    5.653e+00  2.557e-01  2.524e+02  22.107   <2e-16 ***
SCC           -4.943e-02  8.468e-02  2.551e+02  -0.584    0.560    
outdegree     -8.389e-03  5.192e-02  1.973e+03  -0.162    0.872    
SCC:outdegree  1.890e-02  1.667e-02  1.968e+03   1.134    0.257    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) SCC    outdgr
SCC         -0.968              
outdegree   -0.301  0.285       
SCC:outdegr  0.294 -0.298 -0.967
m<-lmer( IM ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: IM ~ SE * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 6655.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.4750 -0.4369  0.1990  0.6372  2.3341 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.3766   0.6137  
 Residual             1.3151   1.1468  
Number of obs: 2056, groups:  subID, 208

Fixed effects:
               Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)     5.90386    0.25482  309.18613  23.168   <2e-16 ***
SE             -0.03643    0.11127  314.85893  -0.327   0.7436    
outdegree       0.10445    0.05443 2047.27892   1.919   0.0551 .  
SE:outdegree   -0.01110    0.02538 2046.40030  -0.437   0.6619    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) SE     outdgr
SE          -0.974              
outdegree   -0.363  0.365       
SE:outdegre  0.340 -0.365 -0.967
m<-lmer( IM ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: IM ~ SAM * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 6535.4

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.4798 -0.3985  0.2003  0.6491  2.3147 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.3355   0.5793  
 Residual             1.3173   1.1477  
Number of obs: 2022, groups:  subID, 205

Fixed effects:
                Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)      5.00158    0.25048  329.65913  19.968  < 2e-16 ***
SAM              0.26042    0.07690  317.61137   3.387 0.000797 ***
outdegree        0.19075    0.07188 2001.96114   2.654 0.008023 ** 
SAM:outdegree   -0.03425    0.02156 1993.74243  -1.589 0.112277    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) SAM    outdgr
SAM         -0.975              
outdegree   -0.395  0.381       
SAM:outdegr  0.390 -0.394 -0.981
m<-lmer( IM ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: IM ~ CESD * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 6649.6

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.4529 -0.4378  0.1831  0.6461  2.3316 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.376    0.6132  
 Residual             1.311    1.1450  
Number of obs: 2056, groups:  subID, 208

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)       5.52096    0.24978  312.02387  22.103  < 2e-16 ***
CESD              0.13318    0.10906  304.74086   1.221 0.222960    
outdegree         0.23630    0.06230 2046.38699   3.793 0.000153 ***
CESD:outdegree   -0.06810    0.02677 2043.66047  -2.544 0.011040 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) CESD   outdgr
CESD        -0.973              
outdegree   -0.376  0.363       
CESD:outdgr  0.368 -0.375 -0.975
m<-lmer( IM ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: IM ~ SOS * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 6656.4

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.4750 -0.4254  0.1928  0.6399  2.3352 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.377    0.614   
 Residual             1.315    1.147   
Number of obs: 2056, groups:  subID, 208

Fixed effects:
                Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)      5.67280    0.24750  297.45064  22.921   <2e-16 ***
SOS              0.05134    0.08255  296.32342   0.622   0.5345    
outdegree        0.13255    0.05175 2032.23331   2.561   0.0105 *  
SOS:outdegree   -0.01834    0.01808 2032.65346  -1.015   0.3104    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) SOS    outdgr
SOS         -0.973              
outdegree   -0.359  0.355       
SOS:outdegr  0.338 -0.358 -0.964
m<-lmer( IM ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: IM ~ DS * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 6656.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.4928 -0.4372  0.1999  0.6372  2.3324 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.3759   0.6131  
 Residual             1.3154   1.1469  
Number of obs: 2056, groups:  subID, 208

Fixed effects:
               Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)   5.969e+00  3.569e-01  3.150e+02  16.723   <2e-16 ***
DS           -3.767e-02  8.885e-02  3.096e+02  -0.424    0.672    
outdegree     4.352e-02  8.294e-02  2.052e+03   0.525    0.600    
DS:outdegree  9.859e-03  2.113e-02  2.050e+03   0.467    0.641    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) DS     outdgr
DS          -0.987              
outdegree   -0.366  0.366       
DS:outdegre  0.357 -0.367 -0.986
m<-lmer( IM ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: IM ~ MAIA * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 6649.4

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.4874 -0.4236  0.1942  0.6388  2.3225 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.3635   0.6029  
 Residual             1.3131   1.1459  
Number of obs: 2056, groups:  subID, 208

Fixed effects:
                Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)    5.127e+00  3.415e-01 2.830e+02  15.014   <2e-16 ***
MAIA           1.843e-01  8.905e-02 2.807e+02   2.070   0.0394 *  
outdegree      7.618e-03  6.860e-02 2.051e+03   0.111   0.9116    
MAIA:outdegree 1.852e-02  1.711e-02 2.051e+03   1.082   0.2793    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) MAIA   outdgr
MAIA        -0.986              
outdegree   -0.364  0.349       
MAIA:outdgr  0.366 -0.364 -0.980
m<-lmer( IM ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: IM ~ DT_P * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 6635.3

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.4636 -0.4326  0.1969  0.6580  2.3606 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.3682   0.6068  
 Residual             1.3028   1.1414  
Number of obs: 2056, groups:  subID, 208

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)       5.85663    0.22300  315.33315  26.262  < 2e-16 ***
DT_P             -0.01160    0.08922  324.38151  -0.130    0.897    
outdegree         0.31074    0.05573 2050.41119   5.575 2.80e-08 ***
DT_P:outdegree   -0.09831    0.02313 2051.99969  -4.250 2.24e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) DT_P   outdgr
DT_P        -0.967              
outdegree   -0.378  0.375       
DT_P:outdgr  0.361 -0.381 -0.969
m<-lmer( IM ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: IM ~ DT_M * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 6649.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.4731 -0.4159  0.1939  0.6427  2.3471 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.3648   0.604   
 Residual             1.3128   1.146   
Number of obs: 2056, groups:  subID, 208

Fixed effects:
                 Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)       5.16018    0.26312  263.08052  19.612  < 2e-16 ***
DT_M              0.21244    0.08218  268.96472   2.585 0.010258 *  
outdegree         0.18295    0.05115 2035.55193   3.577 0.000356 ***
DT_M:outdegree   -0.03412    0.01668 2046.37759  -2.046 0.040911 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) DT_M   outdgr
DT_M        -0.976              
outdegree   -0.342  0.341       
DT_M:outdgr  0.327 -0.350 -0.963
m<-lmer( IM ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: IM ~ NFC * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 6657.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.4811 -0.4264  0.1991  0.6356  2.3354 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.3787   0.6154  
 Residual             1.3144   1.1465  
Number of obs: 2056, groups:  subID, 208

Fixed effects:
               Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)   5.729e+00  2.990e-01 3.005e+02  19.159   <2e-16 ***
NFC           2.419e-02  7.569e-02 2.978e+02   0.320    0.750    
outdegree     3.698e-02  6.894e-02 2.039e+03   0.536    0.592    
NFC:outdegree 1.088e-02  1.654e-02 2.039e+03   0.658    0.511    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) NFC    outdgr
NFC         -0.981              
outdegree   -0.353  0.335       
NFC:outdegr  0.354 -0.350 -0.980
m<-lmer( IM ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: IM ~ SCC * outdegree + (1 | subID)
   Data: fullData

REML criterion at convergence: 6652.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.4753 -0.4283  0.2009  0.6488  2.3293 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept) 0.3692   0.6076  
 Residual             1.3138   1.1462  
Number of obs: 2056, groups:  subID, 208

Fixed effects:
                Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)    6.273e+00  2.273e-01  2.956e+02  27.604   <2e-16 ***
SCC           -1.549e-01  7.535e-02  3.000e+02  -2.055   0.0407 *  
outdegree     -1.952e-04  5.433e-02  2.043e+03  -0.004   0.9971    
SCC:outdegree  2.737e-02  1.746e-02  2.040e+03   1.567   0.1172    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) SCC    outdgr
SCC         -0.968              
outdegree   -0.365  0.346       
SCC:outdegr  0.357 -0.362 -0.967
m<-lmer( Clear ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Rep ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)

Indegree Cross-Level Interaction

fullData$PminN <- (fullData$Val_1-fullData$Val_2)
m<-lmer( PminN ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Fund ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Chan ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)
ggpredict(m, terms = c("CESD","indegree")) %>% plot()

m<-lmer( Chan ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)
ggpredict(m, terms = c("SOS","indegree")) %>% plot()

m<-lmer( Chan ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)
ggpredict(m, terms = c("SCC","indegree")) %>% plot()
m<-lmer( Cert ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( IM ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Clear ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)
m<-lmer( Rep ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)

Do people higher in self-esteem have more self than other focused memories?

fullData$SminO <- fullData$SO_1 - fullData$SO_2

m<-lmer( SminO ~ SE + ( 1 | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: SminO ~ SE + (1 | subID)
   Data: fullData

REML criterion at convergence: 17813.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.8301 -0.5846 -0.1584  0.6071  2.8011 

Random effects:
 Groups   Name        Variance Std.Dev.
 subID    (Intercept)  287     16.94   
 Residual             1191     34.50   
Number of obs: 1776, groups:  subID, 206

Fixed effects:
            Estimate Std. Error       df t value Pr(>|t|)   
(Intercept)  20.1316     6.8611 197.7275   2.934  0.00374 **
SE            0.4568     2.9956 200.2941   0.152  0.87896   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
   (Intr)
SE -0.974
fullShort <- do.call(data.frame,                      # Replace Inf in data by NA
                   lapply(fullShort,
                          function(x) replace(x, is.infinite(x), NA)))
corMat <- fullShort %>% select(edgeTot:NFC) %>% cor(fullShort,use="pairwise.complete.obs")

outphm <- pheatmap(corMat, fontsize_row = 6, fontsize_col = 6, angle_col = 45, angle_row =45, width=100, height = 200 )


heatmaply_cor(round(corMat,3), Rowv=outphm[[1]], Colv=outphm[[2]], revC=TRUE, fontsize_row = 2.5, fontsize_col = 2.5, angle_col = 45, angle_row =45,  limits = c(-1, 1), colors = colorRampPalette(rev(brewer.pal(n = 7, name =
  "RdYlBu")))(100) )
fullShort %>% select(vad_comp, MAIA:NFC) %>% corToOne(., "vad_comp")
Loading required package: corrr

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'

Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(referenceVar)` instead of `referenceVar` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.
fullShort %>% select(vad_comp, MAIA:NFC) %>% plotCorToOne(., "vad_comp")
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'

fullShort %>% select(edgeTot, MAIA:NFC) %>% corToOne(., "edgeTot")
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(edgeTot, MAIA:NFC) %>% plotCorToOne(., "edgeTot")
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'

fullShort %>% select(numID, MAIA:NFC) %>% corToOne(., "numID")
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(numID, MAIA:NFC) %>% plotCorToOne(., "numID")
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'

fullShort %>% select(dense, MAIA:NFC) %>% corToOne(., "dense")
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(dense, MAIA:NFC) %>% plotCorToOne(., "dense")
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'

fullShort %>% select(aveDist, MAIA:NFC) %>% corToOne(., "aveDist")
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(aveDist, MAIA:NFC) %>% plotCorToOne(., "aveDist")
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'

fullShort %>% select(Val_1_Homoph, MAIA:NFC) %>% corToOne(., "Val_1_Homoph")
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Val_1_Homoph, MAIA:NFC) %>% plotCorToOne(., "Val_1_Homoph")
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'

fullShort %>% select(Val_2_Homoph, MAIA:NFC) %>% corToOne(., "Val_2_Homoph")
fullShort %>% select(Val_2_Homoph, MAIA:NFC) %>% plotCorToOne(., "Val_2_Homoph")
fullShort %>% select(Fund_Homoph, MAIA:NFC) %>% corToOne(., "Fund_Homoph")
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Fund_Homoph, MAIA:NFC) %>% plotCorToOne(., "Fund_Homoph")
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'

fullShort %>% select(Rep_Homoph, MAIA:NFC) %>% corToOne(., "Rep_Homoph")
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Rep_Homoph, MAIA:NFC) %>% plotCorToOne(., "Rep_Homoph")
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'

fullShort %>% select(Chan_Homoph, MAIA:NFC) %>% corToOne(., "Chan_Homoph")
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Chan_Homoph, MAIA:NFC) %>% plotCorToOne(., "Chan_Homoph")
[1] "All required packages attached"

Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'

---
title: "R Notebook"
output: html_notebook
---



```{r}
library(groundhog)
pkgs <-  c("tidyverse","here", "lmerTest", "sjPlot","broom.mixed", "kableExtra", "ggeffects", "gt", "brms", "bayestestR","ggdist", "pheatmap", "heatmaply","pheatmap","gplots","RColorBrewer")
groundhog.day <- '2022-07-25'
groundhog.library(pkgs, groundhog.day)
here::i_am("Analysis/idmPrelimAnal.Rmd")
```

```{r}
devtools::source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/corToOne.R")
devtools::source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/plotCommAxes.R")
devtools::source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/named.effects.ref.R")
```


```{r}
fullLong <- arrow::read_parquet(here("Data", "longEpMNet.parquet"))
fullShort <- arrow::read_parquet(here("Data","shortEpMNet.parquet"))
fullLong$subID <- as.numeric(fullLong$subID)
fullData <- fullLong %>% full_join(fullShort, by = c("subID"))
```

# Wordcloud

```{r}
#Create a vector containing only the text
text <- as.vector(fullData$memory)
# Create a corpus  
docs <- Corpus(VectorSource(text))
docs <- docs %>%
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))
docs <- tm_map(docs, removeWords, c("the","and"))

dtm <- TermDocumentMatrix(docs) 
matrix <- as.matrix(dtm) 
words <- sort(rowSums(matrix),decreasing=TRUE) 
df <- data.frame(word = names(words),freq=words)

wordcloud(words = df$word, freq = df$freq, min.freq = 1,           max.words=200, random.order=FALSE, rot.per=0.35,            colors=brewer.pal(8, "Dark2"))
```


# H1: People will evaluate more positively, less negatively (i.e., more favorably) on memories with more downstram dependents.

```{r}
m<-glmer(outdegree ~  Val_1 * Val_2 + ( Val_1 + Val_2 | subID), data=fullData,family="poisson")
summary(m)

m<-lmer(Val_1 ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(Val_2 ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)
```

# H2: People will be more certain in memories with more downstream dependents.

```{r}
m<-lmer(Cert ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(Cert ~  strength + ( strength | subID), data=fullData)
summary(m)
```

# H3: Memories with more dependents will be more clearly defined and accessible.

```{r}
m<-lmer(Clear ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(Clear ~  strength + ( strength | subID), data=fullData)
summary(m)
```
# H5: Memories with more dependents will be more fundamental to how people see themselves, and if they were changed, would change the person.

```{r}
m<-lmer(Fund ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(Fund ~  strength + ( strength | subID), data=fullData)
summary(m)
```

# H6: Memories with more dependents will be more important to the person.

## To Self

```{r}
m<-lmer(IM ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(IM ~  strength + ( strength | subID), data=fullData)
summary(m)
```

## To Others

```{r}
m<-lmer(IO ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(IO ~  outdegree * indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)
ggpredict(m, terms = c("Val_1","Val_2")) %>% plot()

m<-lmer(IO ~  strength + ( strength | subID), data=fullData)
summary(m)
```

# H9: People’s self-report of retrospected emotions during an experience will be associated with how positively or negatively they perceive the experience.

```{r}
m<-lmer(Often ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)
```

# H11: People think more often about memories with more memories causing them.

# The more memories that depend on a given memory, the more people believe "This memory changed me"

```{r}
m<-lmer(Chan ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(Chan ~  strength + ( strength | subID), data=fullData)
summary(m)
```

# The more memories that depend on a given memory, the more certain that people feel this experience is representative of who they are.

```{r}
m<-lmer(Rep ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(Rep ~  strength + ( strength | subID), data=fullData)
summary(m)
```

# Exploratory Analyses

## Sentiment of memory will be associated with dependencies

```{r}
m<-lmer(vad_comp.x ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(vad_comp.x ~  strength + ( strength | subID), data=fullData)
summary(m)

m<-lmer(vad_pos.x ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(vad_pos.x ~  strength + ( strength | subID), data=fullData)
summary(m)

m<-lmer(vad_neg.x ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(vad_neg.x ~  strength + ( strength | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer(vad_pos.x ~  indegree*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer(vad_comp.x ~  strength + ( strength | subID), data=fullData)
summary(m)

m<-lmer(vad_pos.x ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(vad_pos.x ~  strength + ( strength | subID), data=fullData)
summary(m)

m<-lmer(vad_neg.x ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(vad_neg.x ~  strength + ( strength | subID), data=fullData)
summary(m)
```




```{r}
m<-lmer(IM ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(IM ~  strength + ( strength | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer(IO ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(IO ~  strength + ( strength | subID), data=fullData)
summary(m)
```

```{r}
m<-glmer(outdegree ~  Val_1*Val_2 + ( Val_1+Val_2 | subID), data=fullData, family="poisson")
summary(m)
ggpredict(m, terms = c("Val_1","Val_2")) %>% plot()
```


```{r}
m<-lmer(Val_1 ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(Val_1 ~  strength + ( strength | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer(Val_2 ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(Val_2 ~  strength + ( strength | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer(Clear ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(Clear ~  strength + ( strength | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer(Breadth ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(Breadth ~  strength + ( strength | subID), data=fullData)
summary(m)
```




```{r}
m<-lmer(Dist ~  outdegree + indegree + ( outdegree + indegree | subID), data=fullData)
summary(m)

m<-lmer(Dist ~  strength + ( strength | subID), data=fullData)
summary(m)
```

```{r}
m<-glmer(outdegree ~  scale(SO_1) * scale(SO_2) + ( 1 | subID), data=fullData,family="poisson")
summary(m)
ggpredict(m, terms = c("SO_1","SO_2")) %>% plot()
```

```{r}
m<-lmer( Fund ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)
```


```{r}
fullData$PminN <- (fullData$Val_1-fullData$Val_2)
m<-lmer( PminN ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer( Fund ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer( Chan ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer( Cert ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer( IM ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer( Clear ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer( Rep ~ SE*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ SAM*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ CESD*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ SOS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ DS*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ MAIA*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ DT_P*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ DT_M*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ NFC*outdegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ SCC*outdegree + ( 1 | subID), data=fullData)
summary(m)
```


## Indegree Cross-Level Interaction

```{r}
fullData$PminN <- (fullData$Val_1-fullData$Val_2)
m<-lmer( PminN ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( PminN ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer( Fund ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Fund ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer( Chan ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)
ggpredict(m, terms = c("CESD","indegree")) %>% plot()

m<-lmer( Chan ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)
ggpredict(m, terms = c("SOS","indegree")) %>% plot()

m<-lmer( Chan ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Chan ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)
ggpredict(m, terms = c("SCC","indegree")) %>% plot()
```

```{r}
m<-lmer( Cert ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Cert ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer( IM ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( IM ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer( Clear ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Clear ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)
```

```{r}
m<-lmer( Rep ~ SE*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ SAM*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ CESD*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ SOS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ DS*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ MAIA*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ DT_P*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ DT_M*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ NFC*indegree + ( 1 | subID), data=fullData)
summary(m)

m<-lmer( Rep ~ SCC*indegree + ( 1 | subID), data=fullData)
summary(m)
```



# Do people higher in self-esteem have more self than other focused memories?

```{r}
fullData$SminO <- fullData$SO_1 - fullData$SO_2

m<-lmer( SminO ~ SE + ( 1 | subID), data=fullData)
summary(m)
```


```{r}
fullShort <- do.call(data.frame,                      # Replace Inf in data by NA
                   lapply(fullShort,
                          function(x) replace(x, is.infinite(x), NA)))
corMat <- fullShort %>% select(edgeTot:NFC) %>% cor(fullShort,use="pairwise.complete.obs")

outphm <- pheatmap(corMat, fontsize_row = 6, fontsize_col = 6, angle_col = 45, angle_row =45, width=100, height = 200 )

heatmaply_cor(round(corMat,3), Rowv=outphm[[1]], Colv=outphm[[2]], revC=TRUE, fontsize_row = 2.5, fontsize_col = 2.5, angle_col = 45, angle_row =45,  limits = c(-1, 1), colors = colorRampPalette(rev(brewer.pal(n = 7, name =
  "RdYlBu")))(100) )
```

```{r}
fullShort %>% select(vad_comp, MAIA:NFC) %>% corToOne(., "vad_comp")
fullShort %>% select(vad_comp, MAIA:NFC) %>% plotCorToOne(., "vad_comp")
```


```{r}
fullShort %>% select(edgeTot, MAIA:NFC) %>% corToOne(., "edgeTot")
fullShort %>% select(edgeTot, MAIA:NFC) %>% plotCorToOne(., "edgeTot")
```

```{r}
fullShort %>% select(numID, MAIA:NFC) %>% corToOne(., "numID")
fullShort %>% select(numID, MAIA:NFC) %>% plotCorToOne(., "numID")
```

```{r}
fullShort %>% select(dense, MAIA:NFC) %>% corToOne(., "dense")
fullShort %>% select(dense, MAIA:NFC) %>% plotCorToOne(., "dense")
```

```{r}
fullShort %>% select(aveDist, MAIA:NFC) %>% corToOne(., "aveDist")
fullShort %>% select(aveDist, MAIA:NFC) %>% plotCorToOne(., "aveDist")
```

```{r}
fullShort %>% select(Val_1_Homoph, MAIA:NFC) %>% corToOne(., "Val_1_Homoph")
fullShort %>% select(Val_1_Homoph, MAIA:NFC) %>% plotCorToOne(., "Val_1_Homoph")
```

```{r}
fullShort %>% select(Val_2_Homoph, MAIA:NFC) %>% corToOne(., "Val_2_Homoph")
fullShort %>% select(Val_2_Homoph, MAIA:NFC) %>% plotCorToOne(., "Val_2_Homoph")
```

```{r}
fullShort %>% select(Fund_Homoph, MAIA:NFC) %>% corToOne(., "Fund_Homoph")
fullShort %>% select(Fund_Homoph, MAIA:NFC) %>% plotCorToOne(., "Fund_Homoph")
```

```{r}
fullShort %>% select(Rep_Homoph, MAIA:NFC) %>% corToOne(., "Rep_Homoph")
fullShort %>% select(Rep_Homoph, MAIA:NFC) %>% plotCorToOne(., "Rep_Homoph")
```

```{r}
fullShort %>% select(Chan_Homoph, MAIA:NFC) %>% corToOne(., "Chan_Homoph")
fullShort %>% select(Chan_Homoph, MAIA:NFC) %>% plotCorToOne(., "Chan_Homoph")
```










